Estimation in Nonlinear Time Series Models I: Stationary Series

摘要

A general framework for analyzing estimates in nonlinear time series models is developed. Ergodic strictly stationary series are treated. General conditions for strong consistency and asymptotic normality are derived both for conditional least squares and maximum likelihood type estimates. Examples are taken from exponential autoregressive, random coefficient autoregressive and bilinear time series models. Some nonstationary models and examples are treated in a sequel to this paper. (Author)